{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T17:01:06Z","timestamp":1783530066206,"version":"3.55.0"},"reference-count":61,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100005089","name":"Beijing Natural Science Foundation","doi-asserted-by":"publisher","award":["4254101"],"award-info":[{"award-number":["4254101"]}],"id":[{"id":"10.13039\/501100005089","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62373029"],"award-info":[{"award-number":["62373029"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Instrum. Meas."],"published-print":{"date-parts":[[2025]]},"DOI":"10.1109\/tim.2025.3547479","type":"journal-article","created":{"date-parts":[[2025,3,3]],"date-time":"2025-03-03T18:30:15Z","timestamp":1741026615000},"page":"1-14","source":"Crossref","is-referenced-by-count":5,"title":["Detecting Multivariate Time Series Anomalies With Cascade Decomposition Consistency"],"prefix":"10.1109","volume":"74","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2365-2477","authenticated-orcid":false,"given":"Ruoheng","family":"Li","sequence":"first","affiliation":[{"name":"School of Electronic and Information Engineering, Beihang University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3832-8963","authenticated-orcid":false,"given":"Zhongyao","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Beihang University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9356-9746","authenticated-orcid":false,"given":"Xi","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Beihang University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7310-4626","authenticated-orcid":false,"given":"Lin","family":"Li","sequence":"additional","affiliation":[{"name":"National Computer Network Emergency Response Technical Team, Coordination Center of China, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5042-7884","authenticated-orcid":false,"given":"Xianbin","family":"Cao","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Beihang University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2019.2906572"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2019.2958010"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TCOMM.2022.3226193"},{"issue":"1","key":"ref4","volume-title":"Outliers in Statistical Data","volume":"3","author":"Barnett","year":"1994"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2023.3285999"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2023.3342863"},{"key":"ref7","first-page":"22419","article-title":"Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting","volume-title":"Proc. NIPS","volume":"34","author":"Wu"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467174"},{"key":"ref9","doi-asserted-by":"crossref","DOI":"10.1007\/0-387-34471-3","volume-title":"Extreme Value Theory: An Introduction","volume":"3","author":"Haan","year":"2006"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1214\/aoms\/1177693050"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2024.3369159"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2022.3225040"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1145\/3572780"},{"key":"ref14","first-page":"1","article-title":"Deep autoencoding Gaussian mixture model for unsupervised anomaly detection","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Zong"},{"key":"ref15","first-page":"2338","article-title":"Masked autoregressive flow for density estimation","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"30","author":"Papamakarios"},{"key":"ref16","first-page":"9099","article-title":"Denoising normalizing flow","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Horvat"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.2992934"},{"key":"ref18","first-page":"1","article-title":"Graph-augmented normalizing flows for anomaly detection of multiple time series","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Dai"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i4.25623"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2022.3223142"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i11.17152"},{"key":"ref22","first-page":"1","article-title":"Anomaly transformer: Time series anomaly detection with association discrepancy","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Xu"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1145\/3530990"},{"key":"ref24","first-page":"1","article-title":"TimeMixer: Decomposable multiscale mixing for time series forecasting","volume-title":"Proc. 12th Int. Conf. Learn. Represent.","author":"Wang"},{"key":"ref25","first-page":"27268","article-title":"FedFormer: Frequency enhanced decomposed transformer for long-term series forecasting","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Zhou"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2024.3403210"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33015409"},{"key":"ref28","first-page":"38775","article-title":"Learning latent seasonal-trend representations for time series forecasting","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"35","author":"Wang"},{"key":"ref29","first-page":"1","article-title":"TimesNet: Temporal 2D-variation modeling for general time series analysis","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Wu"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599295"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219845"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330672"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2022.3212547"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.14778\/3514061.3514067"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2024.111849"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2023.3329098"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3140058"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3128667"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2021.3139696"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1145\/342009.335388"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2008.17"},{"key":"ref42","first-page":"4393","article-title":"Deep one-class classification","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Ruff"},{"key":"ref43","first-page":"1","article-title":"Deep semi-supervised anomaly detection","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Ruff"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-73003-5_196"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-71368-7_8"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1145\/3055366.3055375"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1002\/wics.182"},{"key":"ref48","first-page":"1","article-title":"Likelihood ratios for out-of-distribution detection","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Ren"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2024.3349613"},{"key":"ref50","first-page":"359","article-title":"An introduction to algorithms and the big O notation","volume-title":"Introduction to Programming With Fortran: With Coverage of Fortran 90, 95, 2003, 2008 and 77","author":"Chivers","year":"2003"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref52","first-page":"597","article-title":"On the computational complexity of self-attention","volume-title":"Proc. Int. Conf. Algorithmic Learn. Theory","author":"Keles"},{"issue":"57","key":"ref53","first-page":"1","article-title":"Normalizing flows for probabilistic modeling and inference","volume":"22","author":"Papamakarios","year":"2021","journal-title":"J. Mach. Learn. Res."},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2979049"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403392"},{"key":"ref56","first-page":"1","article-title":"FITS: Modeling time series with 10k parameters","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Xu"},{"key":"ref57","volume-title":"GANF","author":"Dai","year":"2022"},{"key":"ref58","volume-title":"USAD\u2014Unsupervised Anomaly Detection on Multivariate Time Series","author":"Galati","year":"2020"},{"key":"ref59","volume-title":"Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection in PyTorch","author":"Tan","year":"2018"},{"key":"ref60","volume-title":"FITS: Frequency Interpolation Time Series Analysis Baseline","author":"Xu","year":"2024"},{"key":"ref61","volume-title":"MTGFLOW","author":"Zhou","year":"2023"}],"container-title":["IEEE Transactions on Instrumentation and Measurement"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/19\/10764799\/10909338.pdf?arnumber=10909338","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,14]],"date-time":"2025-03-14T05:43:44Z","timestamp":1741931024000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10909338\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":61,"URL":"https:\/\/doi.org\/10.1109\/tim.2025.3547479","relation":{},"ISSN":["0018-9456","1557-9662"],"issn-type":[{"value":"0018-9456","type":"print"},{"value":"1557-9662","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]}}}